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Journal articles on the topic 'Process uncertainty'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Hall, Jim W. "Handling uncertainty in the hydroinformatic process." Journal of Hydroinformatics 5, no. 4 (October 1, 2003): 215–32. http://dx.doi.org/10.2166/hydro.2003.0019.

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Hydroinformatics combines topics of modelling and decision-making, both of which have attracted a great deal of attention outside hydroinformatics from the point of view of uncertainty. Epistemic uncertainties are due to the inevitably incomplete evidence about the dependability of a model or set of competing models. Inherent uncertainties are due to the varying information content inherent in measurements or model predictions, be they probabilistic or fuzzy. Decision-making in management of the aquatic environment is, more often than not, a complex, discursive, multi-player process. The requirement for hydroinformatics systems is to support rather than replace human judgment in this process, a requirement that has significant bearing on the treatment of uncertainty. Furthermore, a formal language is required to encode uncertainty in computer systems. We therefore review the modern mathematics of uncertainty, starting first with probability theory and then extending to fuzzy set theory and possibility theory, the theory of evidence (and its random set counterpart), which generalises probability and possibility theory, and higher-order generalisations. A simple example from coastal hydraulics illustrates how a range of types of uncertain information (including probability distributions, interval measurements and fuzzy sets) can be handled in the types of algebraic or numerical functions that form the kernel of most hydroinformatic systems.

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2

Helquist, Joel H., Amit Deokar, Jordan J. Cox, and Alyssa Walker. "Analyzing process uncertainty through virtual process simulation." Business Process Management Journal 18, no. 1 (February 3, 2012): 4–19. http://dx.doi.org/10.1108/14637151211214984.

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3

Chen, Xiumei, Yufu Ning, Lihui Wang, Shuai Wang, and Hong Huang. "Some Theorems for Inverse Uncertainty Distribution of Uncertain Processes." Symmetry 14, no. 1 (December 23, 2021): 14. http://dx.doi.org/10.3390/sym14010014.

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In real life, indeterminacy and determinacy are symmetric, while indeterminacy is absolute. We are devoted to studying indeterminacy through uncertainty theory. Within the framework of uncertainty theory, uncertain processes are used to model the evolution of uncertain phenomena. The uncertainty distribution and inverse uncertainty distribution of uncertain processes are important tools to describe uncertain processes. An independent increment process is a special uncertain process with independent increments. An important conjecture about inverse uncertainty distribution of an independent increment process has not been solved yet. In this paper, the conjecture is proven, and therefore, a theorem is obtained. Based on this theorem, some other theorems for inverse uncertainty distribution of the monotone function of independent increment processes are investigated. Meanwhile, some examples are given to illustrate the results.

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4

Zhai, Jia, Haitao Zheng, Manying Bai, and Yunyun Jiang. "An Uncertain Alternating Renewal Insurance Risk Model." Mathematical Problems in Engineering 2020 (July 6, 2020): 1–13. http://dx.doi.org/10.1155/2020/3856323.

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The claim process in an insurance risk model with uncertainty is traditionally described by an uncertain renewal reward process. However, the claim process actually includes two processes, which are called the report process and the payment process, respectively. An alternative way is to describe the claim process by an uncertain alternating renewal reward process. Therefore, this paper proposes an insurance risk model under uncertain measure in which the claim process is supposed to be an alternating renewal reward process and the premium process is regarded as a renewal reward process. Then, the paper also gives the inverse uncertainty distribution of the insurance risk process. The expression of ruin index and the uncertainty distribution of the ruin time are derived which both have explicit expressions based on given uncertainty distributions. Finally, several examples are provided to illustrate the modeling ideas.

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5

Hou, Yongchao. "Optimization Model for Uncertain Statistics Based on an Analytic Hierarchy Process." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/594025.

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Uncertain statistics is a methodology for collecting and interpreting the expert’s experimental data by uncertainty theory. In order to estimate uncertainty distributions, an optimization model based on analytic hierarchy process (AHP) and interpolation method is proposed in this paper. In addition, the principle of least squares method is presented to estimate uncertainty distributions with known functional form. Finally, the effectiveness of this method is illustrated by an example.

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6

Jones, Robert T., and Chuck Ryan. "Matching process choice and uncertainty." Business Process Management Journal 8, no. 2 (May 2002): 161–68. http://dx.doi.org/10.1108/14637150210425117.

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7

Danai, Kourosh, and Hsinyung Chin. "Fault Diagnosis With Process Uncertainty." Journal of Dynamic Systems, Measurement, and Control 113, no. 3 (September 1, 1991): 339–43. http://dx.doi.org/10.1115/1.2896416.

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A nonparametric pattern classification method is introduced for fault diagnosis of complex systems. This method represents the fault signatures by the columns of a multi-valued influence matrix (MVIM), and uses adaptation to cope with fault signature variability. In this method, the measurements are monitored on-line and flagged upon the detection of an abnormality. Fault diagnosis is performed by matching this vector of flagged measurements against the columns of the influence matrix. The MVIM method has the capability to assess the diagnosability of the system, and use that as the basis for sensor selection and optimization. It also uses diagnostic error feedback for adaptation, which enables it to estimate its diagnostic model based upon a small number of measurement-fault data.

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8

Ahmed, Shabbir, and Nikolaos V. Sahinidis. "Robust Process Planning under Uncertainty." Industrial & Engineering Chemistry Research 37, no. 5 (May 1998): 1883–92. http://dx.doi.org/10.1021/ie970694t.

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9

Kanyamibwa, Felicien, and J. Keith Ord. "ECONOMIC PROCESS CONTROL UNDER UNCERTAINTY." Production and Operations Management 9, no. 2 (June 2000): 184–202. http://dx.doi.org/10.1111/j.1937-5956.2000.tb00333.x.

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10

Šapić, Srđan, Jovana Savić, and Jovana Filipović. "Uncertainty avoidance in purchase decision process." Marketing 49, no. 3 (2018): 181–91. http://dx.doi.org/10.5937/markt1803181s.

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11

Lio, Waichon, and Lifen Jia. "Uncertain production risk process with breakdowns and its shortage index and shortage time." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 7151–60. http://dx.doi.org/10.3233/jifs-200453.

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Since the practical production is not continuously available and sometimes suffers unexpected breakdowns, this paper applies uncertainty theory to introducing an uncertain production risk process with breakdowns to handle the production problem with uncertain cycle times (consisting of uncertain on-times and uncertain off-times) and uncertain production amounts. The concept of shortage index of the uncertain production risk process with breakdowns is provided and some formulas for the calculation are given. Furthermore, the shortage time of the uncertain production risk process with breakdowns is proposed and its uncertainty distribution is obtained. Finally, some numerical examples are revealed.

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12

van Lier-Walqui, Marcus, Tomislava Vukicevic, and Derek J. Posselt. "Linearization of Microphysical Parameterization Uncertainty Using Multiplicative Process Perturbation Parameters." Monthly Weather Review 142, no. 1 (January 1, 2014): 401–13. http://dx.doi.org/10.1175/mwr-d-13-00076.1.

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Abstract Recent studies have shown the importance of accounting for model physics uncertainty within probabilistic forecasts. Attempts have been made at quantifying this uncertainty in terms of microphysical parameters such as fall speed coefficients, moments of hydrometeor particle size distributions, and hydrometeor densities. It has been found that uncertainty in terms of these “traditional” microphysical parameters is highly non-Gaussian, calling into question the possibility of estimating and propagating this error using Gaussian statistical techniques such as ensemble Kalman methods. Here, a new choice of uncertain control variables is proposed that instead considers uncertainty in individual modeled microphysical processes. These “process parameters” are multiplicative perturbations on contributions of individual modeled microphysical processes to hydrometeor time tendency. The new process parameters provide a natural and appealing choice for the quantification of aleatory microphysical parameterization uncertainty. Results of a nonlinear Monte Carlo parameter estimation experiment for these new process parameters are presented and compared with the results using traditional microphysical parameters as uncertain control variables. Both experiments occur within the context of an idealized one-dimensional simulation of moist convection, under the observational constraint of simulated radar reflectivity. Results indicate that the new process parameters have a more Gaussian character compared with traditional microphysical parameters, likely due to a more linear control on observable model evolution. In addition, posterior forecast distributions using the new control variables (process parameters) are shown to have less bias and variance. These results strongly recommend the use of the new process parameters for an ensemble Kalman-based estimation of microphysical parameterization uncertainty.

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13

Mukherjee, Rajib, and Urmila M. Diwekar. "Optimizing TEG Dehydration Process under Metamodel Uncertainty." Energies 14, no. 19 (September 28, 2021): 6177. http://dx.doi.org/10.3390/en14196177.

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Natural gas processing requires the removal of acidic gases and dehydration using absorption, mainly conducted in tri-ethylene glycol (TEG). The dehydration process is accompanied by the emission of volatile organic compounds, including BTEX. In our previous work, multi-objective optimization was undertaken to determine the optimal operating conditions in terms of the process parameters that can mitigate BTEX emission using data-driven metamodeling and metaheuristic optimization. Data obtained from a process simulation conducted using the ProMax® process simulator were used to develop a metamodel with machine learning techniques to reduce the computational time of the iterations in a robust process simulation. The metamodels were created using limited samples and some underlying phenomena must therefore be excluded. This introduces the so-called metamodeling uncertainty. Thus, the performance of the resulting optimized process variables may be compromised by the lack of adequately accounting for the uncertainty introduced by the metamodel. In the present work, the bias of the metamodel uncertainty was addressed for parameter optimization. An algorithmic framework was developed for parameter optimization, given these uncertainties. In this framework, metamodel uncertainties are quantified using real model data to generate distribution functions. We then use the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve the problem. BTEX mitigation is used as the objective of the optimization. Our algorithm allows the determination of the optimal process condition for BTEX emission mitigation from the TEG dehydration process under metamodel uncertainty. The BONUS algorithm determines optimal process conditions compared to those from the metaheuristic method, resulting in BTEX emission mitigation up to 405.25 ton/yr.

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Vafadarnikjoo, Amin, and Marco Scherz. "A Hybrid Neutrosophic-Grey Analytic Hierarchy Process Method: Decision-Making Modelling in Uncertain Environments." Mathematical Problems in Engineering 2021 (June 18, 2021): 1–18. http://dx.doi.org/10.1155/2021/1239505.

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The analytic hierarchy process (AHP) is recognised as one of the most commonly applied methods in the multiple attribute decision-making (MADM) literature. In the AHP, encompassing uncertainty feature necessitates using suitable uncertainty theories, since dealing efficiently with uncertainty in subjective judgements is of great importance in real-world decision-making problems. The neutrosophic set (NS) theory and grey systems are two reliable uncertainty theories which can bring considerable benefits to uncertain decision-making. The aim of this study is to improve uncertain decision-making by incorporating advantages of the NS and grey systems theories with the AHP in investigating sustainability through agility readiness evaluation in large manufacturing plants. This study pioneers a combined neutrosophic-grey AHP (NG-AHP) method for uncertain decision-making modelling. The applicability of the hybrid NG-AHP method is shown in an illustrative real-case study for agility evaluations in the Iranian steel industry. The computational results indicate the effectiveness of the proposed method in adequately capturing uncertainty in the subjective judgements of decision makers. In addition, the results verify the significance of the research in group decision-making under uncertainty. The practical outcome reveals that, to become a more sustainable agile steel producer in the case country, they should first focus on the “organisation management agility” as the most significant criterion in the assessment followed by “manufacturing process agility,” “product design agility,” “integration of information system,” and “partnership formation capability,” respectively.

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15

Costa, Daniela, Joana Arantes, and José Keating. "A dual-process approach to cooperative decision-making under uncertainty." PLOS ONE 17, no. 3 (March 22, 2022): e0265759. http://dx.doi.org/10.1371/journal.pone.0265759.

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Cooperative behaviors are typically investigated using social dilemmas inserted into scenarios with well-known characteristics. Nonetheless, in real life, group members may be uncertain about what others will decide (social uncertainty) and the characteristics of the dilemma itself (environmental uncertainty). Previous studies have shown that uncertainty reduces the willingness to cooperate. Dual-process approaches to cooperation have given rise to two different views. Some authors argue that deliberation is needed to overrule selfish motives, whereas others argue that intuition favors cooperation. In this work, our goal was to investigate the role of intuitive mental processing on cooperation in a prisoner’s dilemma game involving uncertainty. Our results showed that participants cooperated less with their counterparts as the number of rounds progressed, suggesting a learning process and that intuitive mental processing in the first 50 rounds appears to favor cooperation under both deterministic and stochastic conditions. These results may help clarify the literature’s mixed effects regarding cognitive processing manipulation on cooperation. Developing a better understanding of these effects may improve strategies in social problems involving cooperation under uncertainty and cognitive constraints.

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Liu, Xuejun, Hailong Tang, Xin Zhang, and Min Chen. "Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine." Applied Sciences 11, no. 18 (September 8, 2021): 8333. http://dx.doi.org/10.3390/app11188333.

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The gas turbine engine is a widely used thermodynamic system for aircraft. The demand for quantifying the uncertainty of engine performance is increasing due to the expectation of reliable engine performance design. In this paper, a fast, accurate, and robust uncertainty quantification method is proposed to investigate the impact of component performance uncertainty on the performance of a classical turboshaft engine. The Gaussian process model is firstly utilized to accurately approximate the relationships between inputs and outputs of the engine performance simulation model. Latin hypercube sampling is subsequently employed to perform uncertainty analysis of the engine performance. The accuracy, robustness, and convergence rate of the proposed method are validated by comparing with the Monte Carlo sampling method. Two main scenarios are investigated, where uncertain parameters are considered to be mutually independent and partially correlated, respectively. Finally, the variance-based sensitivity analysis is used to determine the main contributors to the engine performance uncertainty. Both approximation and sampling errors are explained in the uncertainty quantification to give more accurate results. The final results yield new insights about the engine performance uncertainty and the important component performance parameters.

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17

Jeong, Dong Hwi. "Process Optimization under Parameter Uncertainty Conditions in CCU Process." IFAC-PapersOnLine 55, no. 7 (2022): 586–91. http://dx.doi.org/10.1016/j.ifacol.2022.07.507.

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18

Wechsung, A., J. Oldenburg, J. Yu, and A. Polt. "Supporting chemical process design under uncertainty." Brazilian Journal of Chemical Engineering 27, no. 3 (September 2010): 451–60. http://dx.doi.org/10.1590/s0104-66322010000300009.

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19

Carvalho, Rommel N., Kathryn B. Laskey, and Paulo C. G. Da Costa. "Uncertainty modeling process for semantic technology." PeerJ Computer Science 2 (August 15, 2016): e77. http://dx.doi.org/10.7717/peerj-cs.77.

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The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

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20

JAMIESON, DALE. "Scientific Uncertainty and the Political Process." ANNALS of the American Academy of Political and Social Science 545, no. 1 (May 1996): 35–43. http://dx.doi.org/10.1177/0002716296545001004.

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21

Chou, Shih‐Chien. "Uncertainty management in a process environment." Journal of the Chinese Institute of Engineers 31, no. 4 (June 2008): 625–38. http://dx.doi.org/10.1080/02533839.2008.9671416.

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22

Okamoto, Masaru, Horishi Maru, and Tetsuya Wada. "Study of Uncertainty in Process Modeling." IFAC Proceedings Volumes 30, no. 11 (July 1997): 215–20. http://dx.doi.org/10.1016/s1474-6670(17)42849-7.

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23

Liu, Ming Long, and Nikolaos V. Sahinidis. "Optimization in Process Planning under Uncertainty." Industrial & Engineering Chemistry Research 35, no. 11 (January 1996): 4154–65. http://dx.doi.org/10.1021/ie9504516.

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24

Thompson, M. E. "Uncertainty estimation for stochastic process parameters." Annals of Operations Research 8, no. 1 (December 1987): 195–205. http://dx.doi.org/10.1007/bf02187091.

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25

Latsis, John. "Shackle on time, uncertainty and process." Cambridge Journal of Economics 39, no. 4 (June 10, 2015): 1149–65. http://dx.doi.org/10.1093/cje/bev031.

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26

De Bièvre, P. "Uncertainty assessment is an evaluation process." Accreditation and Quality Assurance 3, no. 10 (October 5, 1998): 391. http://dx.doi.org/10.1007/s007690050269.

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27

Markowski, Adam S., M. Sam Mannan, Agata Kotynia (Bigoszewska), and Dorota Siuta. "Uncertainty aspects in process safety analysis." Journal of Loss Prevention in the Process Industries 23, no. 3 (May 2010): 446–54. http://dx.doi.org/10.1016/j.jlp.2010.02.005.

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28

Pistikopoulos, E. "Uncertainty in process design and operations." Computers & Chemical Engineering 19, no. 1 (June 11, 1995): S553—S563. http://dx.doi.org/10.1016/0098-1354(95)00119-m.

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Pistikopoulos, E. N. "Uncertainty in process design and operations." Computers & Chemical Engineering 19 (June 1995): 553–63. http://dx.doi.org/10.1016/0098-1354(95)87094-6.

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30

Coveney, Sam, Cesare Corrado, Caroline H. Roney, Daniel O’Hare, Steven E. Williams, Mark D. O’Neill, Steven A. Niederer, Richard H. Clayton, Jeremy E. Oakley, and Richard D. Wilkinson. "Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2173 (May 25, 2020): 20190345. http://dx.doi.org/10.1098/rsta.2019.0345.

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In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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Volodymyr, ONYSHCHENKO. "THE NATURE OF THE ECONOMIC PROCESS." Herald of Kyiv National University of Trade and Economics 135, no. 1 (February 24, 2021): 23–40. http://dx.doi.org/10.31617/visnik.knute.2021(135)02.

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Background. Today there are questions that need to be answered, namely: what is the "uncertainty" in the discourse of economic theory and how to take it into account in our economic strategies and processes; how moral and ethical norms can affect our understanding and practice of formation and implementation of economic processes. Analysis of recent researches and publications. The studies of D. North, F. Knight, I. Prigozhin, and E. Laslo are devoted to the problem of uncertainty of economic processes, in which uncertainty is considered in the ontological aspect. Fundamental studies of moral and ethical norms of economic development were carried out by S. Bowles, J. Rawls and A. Sen etс. The aim of the article is to clarify the causal and axiological contexts of the economic process. Materials and methods. The works of native and foreign specialists were the materials of the research. General scientific research methods such as historical, analysis, synthesis and abstraction have been used in the article. Results. Uncertainty is inherent in a market economy and its processes. It is argued that the economic process is nonlinear, its outcome is probabilistic, and may be uncertain. Human economic behaviour is mainly determined by institutions, especially within the economic process. It is proved that there is an organic connection between neoclassical and behavioural theoretical concepts of human economic behaviour. The difference in approaches lies in the levels of generalization of the object and subject of research. The moral and ethical context of the economic process can be taken into account in the models of the economic process, but only as an imperative of "economic justice". The algorithm of finding a "fair solution" involves the approximation of the state of coordination of interests to equilibrium. Conclusion. It is necessary to continue researching the problems of causality of the economic process, which will contribute to the validity of forecasts. The moral and ethical context of the economic process requires expansion of the concept of its effectiveness and the development of modeling methods. Keywords: economic process, evolution, intentionality, uncertainty, institutions, economic behaviour, moral and ethical content, justice.

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Tuczyński, Tomasz, and Jerzy Stopa. "Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm." Energies 16, no. 3 (January 20, 2023): 1153. http://dx.doi.org/10.3390/en16031153.

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Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

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Würtenberger, Jan, Sebastian Gramlich, Tillmann Freund, Julian Lotz, Maximilian Zocholl, and Hermann Kloberdanz. "Uncertainty in Product Modelling within the Development Process." Applied Mechanics and Materials 807 (November 2015): 89–98. http://dx.doi.org/10.4028/www.scientific.net/amm.807.89.

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This paper gives an overview about how to locate uncertainty in product modelling within the development process. Therefore, the process of product modelling is systematized with the help of characteristics of product models and typical working steps to develop a product model. Based on that, it is possible to distinguish between product modelling uncertainty, mathematic modelling uncertainty, parameter uncertainty, simulation uncertainty and product model uncertainty.

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Rooney, William C., and Lorenz T. Biegler. "Optimal process design with model parameter uncertainty and process variability." AIChE Journal 49, no. 2 (February 2003): 438–49. http://dx.doi.org/10.1002/aic.690490214.

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Li, Zhi Yong, Yu Qing Zhao, and Xue Zou. "The Research for Uncertainty Heat Transfer Process of Phase Change Thermal Storage Based on Monte Carlo Method." Applied Mechanics and Materials 291-294 (February 2013): 632–35. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.632.

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Because of phase change materials (PCMs)’ composition, machining error, measuring error and other factors, the PCMs’ thermal physical properties, geometric properties, etc are usually uncertain. Phase change heat transfer process is an uncertainty heat transfer process. In this paper, it is considered factors’ uncertainty influencing phase change thermal storage heat transfer process. Heat transfer model of phase change thermal storage is established. And the uncertainty phase change heat transfer process is analysis based on Monte Carlo method. The experiment shows that the temperature of PCMs varied between the upper bound and lower bound of calculations. Comparison between simulation results of the model and experimental data implies that it is necessary to consider influencing factor’s uncertainty in phase change thermal storage heat transfer analysis.

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Gu, Yuanyuan, Simeng Gu, Yi Lei, and Hong Li. "From Uncertainty to Anxiety: How Uncertainty Fuels Anxiety in a Process Mediated by Intolerance of Uncertainty." Neural Plasticity 2020 (November 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/8866386.

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Uncertainty about future events may lead to worry, anxiety, even inability to function. The highly related concept—intolerance of uncertainty (IU)—emerged in the early 1990s, which is further developed into a transdiagnostic risk factor in multiple forms of anxiety disorders. Interests in uncertainty and intolerance of uncertainty have rapidly increased in recent years; little is known about the construct and phenomenology of uncertainty and IU and the association between them. In an attempt to reveal the nature of two concepts, we reviewed broad literature surrounding uncertainty and intolerance of uncertainty (IU). We followed the process in which the whole IU theory developed and extended, including two aspects: (1) from uncertainty to intolerance of uncertainty and (2) definition of uncertainty and intolerance of uncertainty, and further concluded uncertainty fuels to negative emotions, biased expectancy, and inflexible response. Secondly, this paper summarized the experimental research concerning uncertainty and IU, consisted of three parts: (1) uncertainty-based research, (2) measurements of IU, and (3) domain-specific IU. Lastly, we pointed out what remains unknown and needed to be investigated in future research. This result provides a comprehensive overview in this domain, enhancing our understanding of uncertainty and IU and contributing to further theoretical and empirical explorations.

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Luthfa, Sabrina. "A study of how uncertainty emerges in the uncertainty-embedded innovation process." Journal of Innovation Management 7, no. 1 (May 27, 2019): 46–79. http://dx.doi.org/10.24840/2183-0606_007.001_0005.

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This paper aims to understand about how uncertainty emerges in the innovation process. Since uncertainty is embedded in the innovation process, to understand how uncertainty emerges in the process one needs to understand how innovation process unfolds over time. Since an innovation process involves various resource recombination activities occurring in several phases, to understand how innovation process unfolds one needs understand “how do various resource recombination activities occur over time for the creation of novelty?” This knowledge would enable us to understand the conditions under which vital activities of resource recombination can/cannot be undertaken and coordinated as well as would allow us to understand the underlying decisions made by the innovators for their efficient undertaking and coordination. This paper investigates the innovation process in two companies through performing qualitative study. The innovation processes are analysed in the light of a conceptual model developed based on the Dubois’ (1994) End-product related activity structure model, Håkansson’s (1987) “ARA model” and Goldratt’s (1997) “Critical chain concept”. The findings suggest that uncertainty emerges in the innovation process in a cycle of interaction with resource void, activity void and actors’ limited cognition due to lack of knowledge, undue optimism, and rationally justified reason for disregarding information. Accordingly, a great deal of compromises is made while undertaking the activities.

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Field, Joy M., Larry P. Ritzman, M. Hossein Safizadeh, and Charles E. Downing. "Uncertainty Reduction Approaches, Uncertainty Coping Approaches, and Process Performance in Financial Services." Decision Sciences 37, no. 2 (May 2006): 149–75. http://dx.doi.org/10.1111/j.1540-5915.2006.00120.x.

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Zhang, Qing Shan, and Xiao Xia Qi. "The MPS Model of Node Enterprises in Manufacturing Network under Uncertain Environment." Applied Mechanics and Materials 433-435 (October 2013): 2381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.2381.

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External environment, information retrieval and productive process exist many uncertain factors.When making production plan,enterprises must take the uncertain parameters into account.This paper summarizes the characteristics of manufacturing network and define the node enterperise in manufacturing network;It discusses information uncertainty in manufacturing network and uncertainty in the process of production.It has in depth analysis of MRP formulation process under the environment.The perspective and methods of research on MRP has been given and it has certain enlightenment function for further research.

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Li, Zhi Yong, Zheng Yong Wang, Qu Fan, and Zhan Wu. "The Interval Analysis for Uncertainty Heat Transfer Process of Phase Change Thermal Storage Based on Perturbation Method." Advanced Materials Research 838-841 (November 2013): 1939–43. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1939.

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Due to phase change materials (PCMs) composition, machining error, measuring error and other factors, the PCMs thermal physical properties, geometric properties, etc are usually uncertain. As a result, phase change heat transfer process is an uncertainty heat transfer process. But at present, heat transfer characteristics research on phase change thermal storage are all based on certainty heat transfer models (Taken uncertainty factors as certainty factors). In this paper, it is considered factors uncertainty influencing phase change thermal storage heat transfer process. By looked on the variation scope of influence factors as "interval number", based on interval mathematics, perturbation method and finite difference method, "interval number" heat transfer model of phase change thermal storage is established. In this model, the uncertainty variables are decomposed into the sum of the nominal value and the deviation value. PCM uncertainty temperature field can be determined by calculated nominal value and the deviation value of PCM temperature field separately. Comparison between simulation results of the model and experimental data implies that it is necessary to consider influencing factors uncertainty in phase change thermal storage heat transfer analysis.

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ESKANDARI, HAMIDREZA, and LUIS RABELO. "HANDLING UNCERTAINTY IN THE ANALYTIC HIERARCHY PROCESS: A STOCHASTIC APPROACH." International Journal of Information Technology & Decision Making 06, no. 01 (March 2007): 177–89. http://dx.doi.org/10.1142/s0219622007002356.

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This paper describes a methodology for handling the propagation of uncertainty in the analytic hierarchy process (AHP). In real applications, the pairwise comparisons are usually subject to judgmental errors and are inconsistent and conflicting with each other. Therefore, the weight point estimates provided by the eigenvector method are necessarily approximate. This uncertainty associated with subjective judgmental errors may affect the rank order of decision alternatives. A new stochastic approach is presented to capture the uncertain behavior of the global AHP weights. This approach could help decision makers gain insight into how the imprecision in judgment ratios may affect their choice toward the best solution and how the best alternative(s) may be identified with certain confidence. The proposed approach is applied to the example problem introduced by Saaty for the best high school selection to illustrate the concepts introduced in this paper and to prove its usefulness and practicality.

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Bilson, John F. O., Andrew Kumiega, and Ben Van Vliet. "Trading Model Uncertainty and Statistical Process Control." Journal of Trading 5, no. 3 (June 30, 2010): 39–50. http://dx.doi.org/10.3905/jot.2010.5.3.039.

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Pusztai, László, Balázs Kocsi, István Budai, and Lajos Nagy. "Investigation of a production process under uncertainty." Pollack Periodica 15, no. 2 (August 2020): 49–59. http://dx.doi.org/10.1556/606.2020.15.2.5.

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Abstract:A key role of production managers at manufacturing companies is to make economy-based decisions related to production scheduling. If the production is subject to uncertain factors, like human resource or lack of standardization, production planning becomes difficult and calls for advanced models that are tailored to the manufacturing process. This research investigates a real furniture manufacturing system from both managerial and materialflow points of view. Statistical simulation was run on the manufacturing process, where the possible production structures were given. ANOVA analysis was calculated in order to identify those activities that have the most significant influence on the profit.

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Biggio, Luca, Alexander Wieland, Manuel Arias Chao, Iason Kastanis, and Olga Fink. "Uncertainty-Aware Prognosis via Deep Gaussian Process." IEEE Access 9 (2021): 123517–27. http://dx.doi.org/10.1109/access.2021.3110049.

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Dari-Mattiacci, Giuseppe, and Bruno Deffains. "Uncertainty of Law and the Legal Process." Journal of Institutional and Theoretical Economics 163, no. 4 (2007): 627. http://dx.doi.org/10.1628/093245607783242990.

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Rajabalinejad, Mohammad, and Christos Spitas. "Incorporating Uncertainty into the Design Management Process." Design Management Journal 6, no. 1 (October 2011): 52–67. http://dx.doi.org/10.1111/j.1948-7177.2011.00022.x.

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Morosov, André Luís, and Denis José Schiozer. "Field-Development Process Revealing Uncertainty-Assessment Pitfalls." SPE Reservoir Evaluation & Engineering 20, no. 03 (August 1, 2017): 765–78. http://dx.doi.org/10.2118/180094-pa.

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Vassiliadis, C. G., and E. N. Pistikopoulos. "Process design and maintenance optimization under uncertainty." Computers & Chemical Engineering 23 (June 1999): S555—S558. http://dx.doi.org/10.1016/s0098-1354(99)80137-9.

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Vassiliadis, C. G., and E. N. Pistikopoulos. "Maintenance scheduling and process optimization under uncertainty." Computers & Chemical Engineering 25, no. 2-3 (March 2001): 217–36. http://dx.doi.org/10.1016/s0098-1354(00)00647-5.

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Li, Zukui, and Marianthi G. Ierapetritou. "Robust Optimization for Process Scheduling Under Uncertainty." Industrial & Engineering Chemistry Research 47, no. 12 (June 2008): 4148–57. http://dx.doi.org/10.1021/ie071431u.

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